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Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 10601–10650 of 17610 papers

TitleStatusHype
PURPLE: Making a Large Language Model a Better SQL Writer—0
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions—0
Pushdown Automata in Statistical Machine Translation—0
Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features—0
Pushing The Limit of LLM Capacity for Text Classification—0
Pushing the Limits of Non-Autoregressive Speech Recognition—0
Putting It All into Context: Simplifying Agents with LCLMs—0
Putting Machine Translation in Context with the Noisy Channel Model—0
Better Document-Level Machine Translation with Bayes' Rule—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
PV-VLM: A Multimodal Vision-Language Approach Incorporating Sky Images for Intra-Hour Photovoltaic Power Forecasting—0
PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation—0
PyEvalAI: AI-assisted evaluation of Jupyter Notebooks for immediate personalized feedback—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
q2d: Turning Questions into Dialogs to Teach Models How to Search—0
QA-Expand: Multi-Question Answer Generation for Enhanced Query Expansion in Information Retrieval—0
Q-Agent: Quality-Driven Chain-of-Thought Image Restoration Agent through Robust Multimodal Large Language Model—0
QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation—0
QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus—0
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus—0
QCMUQ@QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling Correction—0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
QCRI Machine Translation Systems for IWSLT 16—0
QCRI@QALB-2015 Shared Task: Correction of Arabic Text for Native and Non-Native Speakers' Errors—0
QCRI’s Machine Translation Systems for IWSLT’16—0
qDKT: Question-centric Deep Knowledge Tracing—0
QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning—0
QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs—0
QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation—0
Q-Heart: ECG Question Answering via Knowledge-Informed Multimodal LLMs—0
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model—0
Qibo: A Large Language Model for Traditional Chinese Medicine—0
QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free Visual Document Understanding—0
PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection—0
QOG:Question and Options Generation based on Language Model—0
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning—0
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning—0
QuAILoRA: Quantization-Aware Initialization for LoRA—0
QuakeBERT: Accurate Classification of Social Media Texts for Rapid Earthquake Impact Assessment—0
Qualitative investigation of the display of speech recognition results for communication with deaf people—0
Quality estimation for Machine Translation output using linguistic analysis and decoding features—0
QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks—0
QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model—0
Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks—0
Quantification and Analysis of Scientific Language Variation Across Research Fields—0
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks—0
Quantifying and Analyzing Entity-level Memorization in Large Language Models—0
Quantifying Context Overlap for Training Word Embeddings—0
Quantifying Geospatial in the Common Crawl Corpus—0
Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified